2D DENSITY PLOT

A 2D density plot or 2D histogram is an extension of the well known histogram. It shows the distribution of values in a data set across the range of two quantitative variables. It is really

useful to avoid over plotting in a scatterplot. If you have too many dots, the 2D density plot counts the number of observations within a particular area of the 2D space. This specific area can be

a square or a hexagon (hexbin). You can also estimate a 2D kernel density estimation and represent it with contours.

Note that this online course has a chapter dedicated to 2D arrays visualization.

Sponsors

From overlapping scatterplot to 2D density

#86 Avoid overlapping with 2D density

Contour plot

#80 Contour plot with seaborn

#80 Density plot with seaborn

#80 Contour plot with seaborn

2D Histogram

#83 adjust bin size of 2D histogram

#83 adjust bin size of 2D histogram

#83 Change color palette of 2D Histogram

#83 2D histogram with colorer

Hexbin

#84 Hexbin plot with Matplotlib

#84 Change grid size in Hexbin

#84 Color in Hexbin plot

#84 Add color bar to hex bin plot

2D Density

#85 Color of 2D density plot

#85 2D density plot with matplotlib

Marginal plots

If you have a huge amount of dots on your graphic, it is advised to represent the marginal distribution of both the X and Y variables. This is easy to do using the jointplot() function of the Seaborn library.

#82 Default Marginal plot

#82 Custom marginal area

#82 2D contour with marginal plots

#82 Custom color of marginal plot

related

Heatmap

Bubble plot

Scatterplot

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